{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "771f669f-065a-4b0d-b5d5-9aa0119c43fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 1.609941\n",
      "Train Epoch: 1 [10000/60000 (17%)]\tLoss: 1.018354\n",
      "Train Epoch: 1 [20000/60000 (33%)]\tLoss: 0.966024\n",
      "Train Epoch: 1 [30000/60000 (50%)]\tLoss: 0.960433\n",
      "Train Epoch: 1 [40000/60000 (67%)]\tLoss: 0.967788\n",
      "Train Epoch: 1 [50000/60000 (83%)]\tLoss: 0.944688\n",
      "\n",
      "Test set: Average loss: 0.0094, Accuracy: 9712/10000 (97%)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.952716\n",
      "Train Epoch: 2 [10000/60000 (17%)]\tLoss: 0.957883\n",
      "Train Epoch: 2 [20000/60000 (33%)]\tLoss: 0.906937\n",
      "Train Epoch: 2 [30000/60000 (50%)]\tLoss: 0.937866\n",
      "Train Epoch: 2 [40000/60000 (67%)]\tLoss: 0.933759\n",
      "Train Epoch: 2 [50000/60000 (83%)]\tLoss: 0.925578\n",
      "\n",
      "Test set: Average loss: 0.0093, Accuracy: 9771/10000 (98%)\n",
      "\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.924476\n",
      "Train Epoch: 3 [10000/60000 (17%)]\tLoss: 0.912333\n",
      "Train Epoch: 3 [20000/60000 (33%)]\tLoss: 0.913200\n",
      "Train Epoch: 3 [30000/60000 (50%)]\tLoss: 0.918556\n",
      "Train Epoch: 3 [40000/60000 (67%)]\tLoss: 0.906669\n",
      "Train Epoch: 3 [50000/60000 (83%)]\tLoss: 0.917957\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9836/10000 (98%)\n",
      "\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.910508\n",
      "Train Epoch: 4 [10000/60000 (17%)]\tLoss: 0.923663\n",
      "Train Epoch: 4 [20000/60000 (33%)]\tLoss: 0.913465\n",
      "Train Epoch: 4 [30000/60000 (50%)]\tLoss: 0.915024\n",
      "Train Epoch: 4 [40000/60000 (67%)]\tLoss: 0.914928\n",
      "Train Epoch: 4 [50000/60000 (83%)]\tLoss: 0.923727\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9857/10000 (99%)\n",
      "\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.933191\n",
      "Train Epoch: 5 [10000/60000 (17%)]\tLoss: 0.915570\n",
      "Train Epoch: 5 [20000/60000 (33%)]\tLoss: 0.916261\n",
      "Train Epoch: 5 [30000/60000 (50%)]\tLoss: 0.909053\n",
      "Train Epoch: 5 [40000/60000 (67%)]\tLoss: 0.910863\n",
      "Train Epoch: 5 [50000/60000 (83%)]\tLoss: 0.912639\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9844/10000 (98%)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.917252\n",
      "Train Epoch: 6 [10000/60000 (17%)]\tLoss: 0.932483\n",
      "Train Epoch: 6 [20000/60000 (33%)]\tLoss: 0.909827\n",
      "Train Epoch: 6 [30000/60000 (50%)]\tLoss: 0.913279\n",
      "Train Epoch: 6 [40000/60000 (67%)]\tLoss: 0.915090\n",
      "Train Epoch: 6 [50000/60000 (83%)]\tLoss: 0.918999\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9865/10000 (99%)\n",
      "\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.905079\n",
      "Train Epoch: 7 [10000/60000 (17%)]\tLoss: 0.905075\n",
      "Train Epoch: 7 [20000/60000 (33%)]\tLoss: 0.905043\n",
      "Train Epoch: 7 [30000/60000 (50%)]\tLoss: 0.924446\n",
      "Train Epoch: 7 [40000/60000 (67%)]\tLoss: 0.918102\n",
      "Train Epoch: 7 [50000/60000 (83%)]\tLoss: 0.904861\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9870/10000 (99%)\n",
      "\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.916832\n",
      "Train Epoch: 8 [10000/60000 (17%)]\tLoss: 0.908343\n",
      "Train Epoch: 8 [20000/60000 (33%)]\tLoss: 0.916452\n",
      "Train Epoch: 8 [30000/60000 (50%)]\tLoss: 0.915477\n",
      "Train Epoch: 8 [40000/60000 (67%)]\tLoss: 0.907949\n",
      "Train Epoch: 8 [50000/60000 (83%)]\tLoss: 0.906689\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9865/10000 (99%)\n",
      "\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.905347\n",
      "Train Epoch: 9 [10000/60000 (17%)]\tLoss: 0.905989\n",
      "Train Epoch: 9 [20000/60000 (33%)]\tLoss: 0.906103\n",
      "Train Epoch: 9 [30000/60000 (50%)]\tLoss: 0.905219\n",
      "Train Epoch: 9 [40000/60000 (67%)]\tLoss: 0.919588\n",
      "Train Epoch: 9 [50000/60000 (83%)]\tLoss: 0.919014\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9879/10000 (99%)\n",
      "\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss: 0.910739\n",
      "Train Epoch: 10 [10000/60000 (17%)]\tLoss: 0.926195\n",
      "Train Epoch: 10 [20000/60000 (33%)]\tLoss: 0.911765\n",
      "Train Epoch: 10 [30000/60000 (50%)]\tLoss: 0.905581\n",
      "Train Epoch: 10 [40000/60000 (67%)]\tLoss: 0.924980\n",
      "Train Epoch: 10 [50000/60000 (83%)]\tLoss: 0.925439\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9889/10000 (99%)\n",
      "\n",
      "Train Epoch: 11 [0/60000 (0%)]\tLoss: 0.910595\n",
      "Train Epoch: 11 [10000/60000 (17%)]\tLoss: 0.905060\n",
      "Train Epoch: 11 [20000/60000 (33%)]\tLoss: 0.905051\n",
      "Train Epoch: 11 [30000/60000 (50%)]\tLoss: 0.906193\n",
      "Train Epoch: 11 [40000/60000 (67%)]\tLoss: 0.905054\n",
      "Train Epoch: 11 [50000/60000 (83%)]\tLoss: 0.911538\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9884/10000 (99%)\n",
      "\n",
      "Train Epoch: 12 [0/60000 (0%)]\tLoss: 0.905201\n",
      "Train Epoch: 12 [10000/60000 (17%)]\tLoss: 0.914851\n",
      "Train Epoch: 12 [20000/60000 (33%)]\tLoss: 0.904981\n",
      "Train Epoch: 12 [30000/60000 (50%)]\tLoss: 0.904853\n",
      "Train Epoch: 12 [40000/60000 (67%)]\tLoss: 0.916523\n",
      "Train Epoch: 12 [50000/60000 (83%)]\tLoss: 0.913080\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9908/10000 (99%)\n",
      "\n",
      "Train Epoch: 13 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 13 [10000/60000 (17%)]\tLoss: 0.904839\n",
      "Train Epoch: 13 [20000/60000 (33%)]\tLoss: 0.904871\n",
      "Train Epoch: 13 [30000/60000 (50%)]\tLoss: 0.904838\n",
      "Train Epoch: 13 [40000/60000 (67%)]\tLoss: 0.905228\n",
      "Train Epoch: 13 [50000/60000 (83%)]\tLoss: 0.926427\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9907/10000 (99%)\n",
      "\n",
      "Train Epoch: 14 [0/60000 (0%)]\tLoss: 0.905538\n",
      "Train Epoch: 14 [10000/60000 (17%)]\tLoss: 0.906496\n",
      "Train Epoch: 14 [20000/60000 (33%)]\tLoss: 0.904864\n",
      "Train Epoch: 14 [30000/60000 (50%)]\tLoss: 0.916835\n",
      "Train Epoch: 14 [40000/60000 (67%)]\tLoss: 0.904834\n",
      "Train Epoch: 14 [50000/60000 (83%)]\tLoss: 0.904946\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9902/10000 (99%)\n",
      "\n",
      "Train Epoch: 15 [0/60000 (0%)]\tLoss: 0.917173\n",
      "Train Epoch: 15 [10000/60000 (17%)]\tLoss: 0.917176\n",
      "Train Epoch: 15 [20000/60000 (33%)]\tLoss: 0.914756\n",
      "Train Epoch: 15 [30000/60000 (50%)]\tLoss: 0.905294\n",
      "Train Epoch: 15 [40000/60000 (67%)]\tLoss: 0.904857\n",
      "Train Epoch: 15 [50000/60000 (83%)]\tLoss: 0.914833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9919/10000 (99%)\n",
      "\n",
      "Train Epoch: 16 [0/60000 (0%)]\tLoss: 0.931562\n",
      "Train Epoch: 16 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 16 [20000/60000 (33%)]\tLoss: 0.904983\n",
      "Train Epoch: 16 [30000/60000 (50%)]\tLoss: 0.904836\n",
      "Train Epoch: 16 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 16 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9914/10000 (99%)\n",
      "\n",
      "Train Epoch: 17 [0/60000 (0%)]\tLoss: 0.914707\n",
      "Train Epoch: 17 [10000/60000 (17%)]\tLoss: 0.914840\n",
      "Train Epoch: 17 [20000/60000 (33%)]\tLoss: 0.904845\n",
      "Train Epoch: 17 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 17 [40000/60000 (67%)]\tLoss: 0.914310\n",
      "Train Epoch: 17 [50000/60000 (83%)]\tLoss: 0.911653\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9901/10000 (99%)\n",
      "\n",
      "Train Epoch: 18 [0/60000 (0%)]\tLoss: 0.904854\n",
      "Train Epoch: 18 [10000/60000 (17%)]\tLoss: 0.914454\n",
      "Train Epoch: 18 [20000/60000 (33%)]\tLoss: 0.923695\n",
      "Train Epoch: 18 [30000/60000 (50%)]\tLoss: 0.905019\n",
      "Train Epoch: 18 [40000/60000 (67%)]\tLoss: 0.916225\n",
      "Train Epoch: 18 [50000/60000 (83%)]\tLoss: 0.914859\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9910/10000 (99%)\n",
      "\n",
      "Train Epoch: 19 [0/60000 (0%)]\tLoss: 0.904878\n",
      "Train Epoch: 19 [10000/60000 (17%)]\tLoss: 0.905351\n",
      "Train Epoch: 19 [20000/60000 (33%)]\tLoss: 0.904851\n",
      "Train Epoch: 19 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 19 [40000/60000 (67%)]\tLoss: 0.914870\n",
      "Train Epoch: 19 [50000/60000 (83%)]\tLoss: 0.904959\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9886/10000 (99%)\n",
      "\n",
      "Train Epoch: 20 [0/60000 (0%)]\tLoss: 0.905527\n",
      "Train Epoch: 20 [10000/60000 (17%)]\tLoss: 0.904858\n",
      "Train Epoch: 20 [20000/60000 (33%)]\tLoss: 0.914226\n",
      "Train Epoch: 20 [30000/60000 (50%)]\tLoss: 0.908858\n",
      "Train Epoch: 20 [40000/60000 (67%)]\tLoss: 0.924019\n",
      "Train Epoch: 20 [50000/60000 (83%)]\tLoss: 0.923693\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9899/10000 (99%)\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 21 [0/60000 (0%)]\tLoss: 0.914833\n",
      "Train Epoch: 21 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 21 [20000/60000 (33%)]\tLoss: 0.916122\n",
      "Train Epoch: 21 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 21 [40000/60000 (67%)]\tLoss: 0.914828\n",
      "Train Epoch: 21 [50000/60000 (83%)]\tLoss: 0.905139\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9874/10000 (99%)\n",
      "\n",
      "Train Epoch: 22 [0/60000 (0%)]\tLoss: 0.904851\n",
      "Train Epoch: 22 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 22 [20000/60000 (33%)]\tLoss: 0.904834\n",
      "Train Epoch: 22 [30000/60000 (50%)]\tLoss: 0.914833\n",
      "Train Epoch: 22 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 22 [50000/60000 (83%)]\tLoss: 0.914399\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9910/10000 (99%)\n",
      "\n",
      "Train Epoch: 23 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 23 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 23 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 23 [30000/60000 (50%)]\tLoss: 0.923209\n",
      "Train Epoch: 23 [40000/60000 (67%)]\tLoss: 0.912102\n",
      "Train Epoch: 23 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9871/10000 (99%)\n",
      "\n",
      "Train Epoch: 24 [0/60000 (0%)]\tLoss: 0.904840\n",
      "Train Epoch: 24 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 24 [20000/60000 (33%)]\tLoss: 0.909194\n",
      "Train Epoch: 24 [30000/60000 (50%)]\tLoss: 0.905101\n",
      "Train Epoch: 24 [40000/60000 (67%)]\tLoss: 0.922078\n",
      "Train Epoch: 24 [50000/60000 (83%)]\tLoss: 0.905154\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9914/10000 (99%)\n",
      "\n",
      "Train Epoch: 25 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 25 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 25 [20000/60000 (33%)]\tLoss: 0.907552\n",
      "Train Epoch: 25 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 25 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 25 [50000/60000 (83%)]\tLoss: 0.905292\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9916/10000 (99%)\n",
      "\n",
      "Train Epoch: 26 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 26 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 26 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 26 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 26 [40000/60000 (67%)]\tLoss: 0.904835\n",
      "Train Epoch: 26 [50000/60000 (83%)]\tLoss: 0.923350\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9916/10000 (99%)\n",
      "\n",
      "Train Epoch: 27 [0/60000 (0%)]\tLoss: 0.914242\n",
      "Train Epoch: 27 [10000/60000 (17%)]\tLoss: 0.914221\n",
      "Train Epoch: 27 [20000/60000 (33%)]\tLoss: 0.924191\n",
      "Train Epoch: 27 [30000/60000 (50%)]\tLoss: 0.904846\n",
      "Train Epoch: 27 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 27 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9928/10000 (99%)\n",
      "\n",
      "Train Epoch: 28 [0/60000 (0%)]\tLoss: 0.905054\n",
      "Train Epoch: 28 [10000/60000 (17%)]\tLoss: 0.904836\n",
      "Train Epoch: 28 [20000/60000 (33%)]\tLoss: 0.904927\n",
      "Train Epoch: 28 [30000/60000 (50%)]\tLoss: 0.904834\n",
      "Train Epoch: 28 [40000/60000 (67%)]\tLoss: 0.934832\n",
      "Train Epoch: 28 [50000/60000 (83%)]\tLoss: 0.916814\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9913/10000 (99%)\n",
      "\n",
      "Train Epoch: 29 [0/60000 (0%)]\tLoss: 0.904839\n",
      "Train Epoch: 29 [10000/60000 (17%)]\tLoss: 0.924787\n",
      "Train Epoch: 29 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 29 [30000/60000 (50%)]\tLoss: 0.914537\n",
      "Train Epoch: 29 [40000/60000 (67%)]\tLoss: 0.905010\n",
      "Train Epoch: 29 [50000/60000 (83%)]\tLoss: 0.904840\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9902/10000 (99%)\n",
      "\n",
      "Train Epoch: 30 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 30 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 30 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 30 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 30 [40000/60000 (67%)]\tLoss: 0.904839\n",
      "Train Epoch: 30 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9907/10000 (99%)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 31 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 31 [10000/60000 (17%)]\tLoss: 0.924434\n",
      "Train Epoch: 31 [20000/60000 (33%)]\tLoss: 0.904846\n",
      "Train Epoch: 31 [30000/60000 (50%)]\tLoss: 0.904849\n",
      "Train Epoch: 31 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 31 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9892/10000 (99%)\n",
      "\n",
      "Train Epoch: 32 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 32 [10000/60000 (17%)]\tLoss: 0.914833\n",
      "Train Epoch: 32 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 32 [30000/60000 (50%)]\tLoss: 0.914721\n",
      "Train Epoch: 32 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 32 [50000/60000 (83%)]\tLoss: 0.904889\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9903/10000 (99%)\n",
      "\n",
      "Train Epoch: 33 [0/60000 (0%)]\tLoss: 0.915031\n",
      "Train Epoch: 33 [10000/60000 (17%)]\tLoss: 0.904986\n",
      "Train Epoch: 33 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 33 [30000/60000 (50%)]\tLoss: 0.904838\n",
      "Train Epoch: 33 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 33 [50000/60000 (83%)]\tLoss: 0.904834\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9908/10000 (99%)\n",
      "\n",
      "Train Epoch: 34 [0/60000 (0%)]\tLoss: 0.904859\n",
      "Train Epoch: 34 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 34 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 34 [30000/60000 (50%)]\tLoss: 0.914518\n",
      "Train Epoch: 34 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 34 [50000/60000 (83%)]\tLoss: 0.914812\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9920/10000 (99%)\n",
      "\n",
      "Train Epoch: 35 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 35 [10000/60000 (17%)]\tLoss: 0.904841\n",
      "Train Epoch: 35 [20000/60000 (33%)]\tLoss: 0.905746\n",
      "Train Epoch: 35 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 35 [40000/60000 (67%)]\tLoss: 0.904835\n",
      "Train Epoch: 35 [50000/60000 (83%)]\tLoss: 0.904902\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9903/10000 (99%)\n",
      "\n",
      "Train Epoch: 36 [0/60000 (0%)]\tLoss: 0.914365\n",
      "Train Epoch: 36 [10000/60000 (17%)]\tLoss: 0.914365\n",
      "Train Epoch: 36 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 36 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 36 [40000/60000 (67%)]\tLoss: 0.914833\n",
      "Train Epoch: 36 [50000/60000 (83%)]\tLoss: 0.914835\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9911/10000 (99%)\n",
      "\n",
      "Train Epoch: 37 [0/60000 (0%)]\tLoss: 0.904834\n",
      "Train Epoch: 37 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 37 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 37 [30000/60000 (50%)]\tLoss: 0.904836\n",
      "Train Epoch: 37 [40000/60000 (67%)]\tLoss: 0.914828\n",
      "Train Epoch: 37 [50000/60000 (83%)]\tLoss: 0.914818\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9916/10000 (99%)\n",
      "\n",
      "Train Epoch: 38 [0/60000 (0%)]\tLoss: 0.904996\n",
      "Train Epoch: 38 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 38 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 38 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 38 [40000/60000 (67%)]\tLoss: 0.914833\n",
      "Train Epoch: 38 [50000/60000 (83%)]\tLoss: 0.906287\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9913/10000 (99%)\n",
      "\n",
      "Train Epoch: 39 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 39 [10000/60000 (17%)]\tLoss: 0.914601\n",
      "Train Epoch: 39 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 39 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 39 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 39 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9917/10000 (99%)\n",
      "\n",
      "Train Epoch: 40 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 40 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 40 [20000/60000 (33%)]\tLoss: 0.904834\n",
      "Train Epoch: 40 [30000/60000 (50%)]\tLoss: 0.914833\n",
      "Train Epoch: 40 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 40 [50000/60000 (83%)]\tLoss: 0.914833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9904/10000 (99%)\n",
      "\n",
      "Train Epoch: 41 [0/60000 (0%)]\tLoss: 0.904913\n",
      "Train Epoch: 41 [10000/60000 (17%)]\tLoss: 0.904847\n",
      "Train Epoch: 41 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 41 [30000/60000 (50%)]\tLoss: 0.904835\n",
      "Train Epoch: 41 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 41 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9911/10000 (99%)\n",
      "\n",
      "Train Epoch: 42 [0/60000 (0%)]\tLoss: 0.904866\n",
      "Train Epoch: 42 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 42 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 42 [30000/60000 (50%)]\tLoss: 0.914842\n",
      "Train Epoch: 42 [40000/60000 (67%)]\tLoss: 0.904849\n",
      "Train Epoch: 42 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9921/10000 (99%)\n",
      "\n",
      "Train Epoch: 43 [0/60000 (0%)]\tLoss: 0.904922\n",
      "Train Epoch: 43 [10000/60000 (17%)]\tLoss: 0.914833\n",
      "Train Epoch: 43 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 43 [30000/60000 (50%)]\tLoss: 0.904865\n",
      "Train Epoch: 43 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 43 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9907/10000 (99%)\n",
      "\n",
      "Train Epoch: 44 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 44 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 44 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 44 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 44 [40000/60000 (67%)]\tLoss: 0.904838\n",
      "Train Epoch: 44 [50000/60000 (83%)]\tLoss: 0.904848\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9922/10000 (99%)\n",
      "\n",
      "Train Epoch: 45 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 45 [10000/60000 (17%)]\tLoss: 0.914844\n",
      "Train Epoch: 45 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 45 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 45 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 45 [50000/60000 (83%)]\tLoss: 0.904844\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9890/10000 (99%)\n",
      "\n",
      "Train Epoch: 46 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 46 [10000/60000 (17%)]\tLoss: 0.909525\n",
      "Train Epoch: 46 [20000/60000 (33%)]\tLoss: 0.914832\n",
      "Train Epoch: 46 [30000/60000 (50%)]\tLoss: 0.914831\n",
      "Train Epoch: 46 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 46 [50000/60000 (83%)]\tLoss: 0.904863\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9896/10000 (99%)\n",
      "\n",
      "Train Epoch: 47 [0/60000 (0%)]\tLoss: 0.914833\n",
      "Train Epoch: 47 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 47 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 47 [30000/60000 (50%)]\tLoss: 0.904850\n",
      "Train Epoch: 47 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 47 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9924/10000 (99%)\n",
      "\n",
      "Train Epoch: 48 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 48 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 48 [20000/60000 (33%)]\tLoss: 0.921735\n",
      "Train Epoch: 48 [30000/60000 (50%)]\tLoss: 0.914822\n",
      "Train Epoch: 48 [40000/60000 (67%)]\tLoss: 0.904932\n",
      "Train Epoch: 48 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0092, Accuracy: 9895/10000 (99%)\n",
      "\n",
      "Train Epoch: 49 [0/60000 (0%)]\tLoss: 0.914833\n",
      "Train Epoch: 49 [10000/60000 (17%)]\tLoss: 0.904833\n",
      "Train Epoch: 49 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 49 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 49 [40000/60000 (67%)]\tLoss: 0.914833\n",
      "Train Epoch: 49 [50000/60000 (83%)]\tLoss: 0.915546\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9914/10000 (99%)\n",
      "\n",
      "Train Epoch: 50 [0/60000 (0%)]\tLoss: 0.904833\n",
      "Train Epoch: 50 [10000/60000 (17%)]\tLoss: 0.911970\n",
      "Train Epoch: 50 [20000/60000 (33%)]\tLoss: 0.904833\n",
      "Train Epoch: 50 [30000/60000 (50%)]\tLoss: 0.904833\n",
      "Train Epoch: 50 [40000/60000 (67%)]\tLoss: 0.904833\n",
      "Train Epoch: 50 [50000/60000 (83%)]\tLoss: 0.904833\n",
      "\n",
      "Test set: Average loss: 0.0091, Accuracy: 9914/10000 (99%)\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import torch\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 下载 mnist 数据集\n",
    "train_data = torchvision.datasets.MNIST(root='data', train=True, transform=torchvision.transforms.ToTensor(), download=True)\n",
    "test_data = torchvision.datasets.MNIST(root='data', train=False, transform=torchvision.transforms.ToTensor())\n",
    "\n",
    "# 定义 DataLoader 对象\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=100, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=100, shuffle=True)\n",
    "\n",
    "# 定义 CNN 模型的类\n",
    "class CNN(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        # 定义两个卷积层\n",
    "        self.conv1 = torch.nn.Conv2d(1, 16, 3) # 输入通道为 1，输出通道为 16，卷积核大小为 3x3\n",
    "        self.conv2 = torch.nn.Conv2d(16, 32, 3) # 输入通道为 16，输出通道为 32，卷积核大小为 3x3\n",
    "        # 定义两个池化层\n",
    "        self.pool = torch.nn.MaxPool2d(2) # 池化核大小为 2x2\n",
    "        # 定义两个全连接层\n",
    "        self.fc1 = torch.nn.Linear(32 * 5 * 5, 800) # 输入维度为 32 * 5 * 5（经过两次卷积和池化后的特征图大小），输出维度为 800\n",
    "        self.fc2 = torch.nn.Linear(800, 5) # 输入维度为 800，输出维度为 5（类别数）\n",
    "        # 定义一个 softmax 层\n",
    "        self.softmax = torch.nn.Softmax(dim=1) # 在第一个维度上进行 softmax\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 前向传播过程\n",
    "        x = self.pool(torch.nn.functional.relu(self.conv1(x))) # 第一次卷积\n",
    "        x = self.pool(torch.nn.functional.relu(self.conv2(x))) # 第二次卷积\n",
    "        x = x.view(-1, 32 * 5 * 5) # 将特征图展平为一维向量\n",
    "        x = torch.nn.functional.relu(self.fc1(x)) # 第一个全连接层\n",
    "        x = self.fc2(x) # 第二个全连接层\n",
    "        x = self.softmax(x) # softmax 层\n",
    "        return x\n",
    "\n",
    "# 创建一个 CNN 模型的实例\n",
    "model = CNN()\n",
    "\n",
    "# 定义一个损失函数\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "\n",
    "# 定义一个优化器\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 定义两个列表，用于存储每个周期的准确率\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "# 定义一个训练函数\n",
    "def train(epoch):\n",
    "    # 设置模型为训练模式\n",
    "    model.train()\n",
    "    # 初始化训练损失和正确预测的数量\n",
    "    train_loss = 0\n",
    "    correct = 0\n",
    "    # 遍历训练数据\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        # 将数据和标签转换为适合模型的格式\n",
    "        data = data # 数据已经是张量格式，无需转换\n",
    "        target = target // 2 # 将标签为 0 和 1，2 和 3，4 和 5，6 和 7，8 和 9 的数字分别分为一类（共 5 类）\n",
    "        # 将数据和标签放到计算设备上，如 CPU 或 GPU\n",
    "        data = data.to(device)\n",
    "        target = target.to(device)\n",
    "        # 清空之前的梯度\n",
    "        optimizer.zero_grad()\n",
    "        # 前向传播，得到预测结果\n",
    "        output = model(data)\n",
    "        # 计算损失\n",
    "        loss = criterion(output, target)\n",
    "        train_loss += criterion(output, target).item()\n",
    "        # 得到预测的类别，即概率最大的那一类\n",
    "        pred = output.argmax(dim=1, keepdim=True)\n",
    "        # 累加正确预测的数量\n",
    "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "        # 反向传播，计算梯度\n",
    "        loss.backward()\n",
    "        # 更新参数\n",
    "        optimizer.step()\n",
    "        # 打印训练进度和损失信息\n",
    "        if batch_idx % 100 == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item()))\n",
    "    # 将训练准确率添加到列表中\n",
    "    train_acc.append(100. * correct / len(train_loader.dataset))\n",
    "\n",
    "# 定义一个测试函数，添加一个参数 epoch\n",
    "def test(epoch):\n",
    "    # 设置模型为评估模式\n",
    "    model.eval()\n",
    "    # 初始化测试损失和正确预测的数量\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    # 遍历测试数据\n",
    "    with torch.no_grad(): # 不计算梯度，节省内存和时间\n",
    "        for data, target in test_loader:\n",
    "            # 将数据和标签转换为适合模型的格式\n",
    "            data = data # 数据已经是张量格式，无需转换\n",
    "            target = target // 2 # 将标签为 0 和 1，2 和 3，4 和 5，6 和 7，8 和 9 的数字分别分为一类（共 5 类）\n",
    "            # 将数据和标签放到计算设备上，如 CPU 或 GPU\n",
    "            data = data.to(device)\n",
    "            target = target.to(device)\n",
    "            # 前向传播，得到预测结果\n",
    "            output = model(data)\n",
    "            # 累加测试损失\n",
    "            test_loss += criterion(output, target).item()\n",
    "            # 得到预测的类别，即概率最大的那一类\n",
    "            pred = output.argmax(dim=1, keepdim=True)\n",
    "            # 累加正确预测的数量\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "    # 计算并打印平均测试损失和准确率信息\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))\n",
    "    # 将测试准确率添加到列表中\n",
    "    test_acc.append(100. * correct / len(test_loader.dataset))\n",
    "    \n",
    "    # 如果是第 1、5 或 10 个周期，则进行直观验证\n",
    "    if epoch in [1, 5, 20 , 30 ,50]:\n",
    "        # 遍历测试数据的前 10 个样本\n",
    "        for i in range(10):\n",
    "            # 获取数据和标签，并分开存储\n",
    "            data, target = test_data[i] \n",
    "            # 将数据转换为适合模型的格式，并增加一个批次维度\n",
    "            data = data.unsqueeze(0) \n",
    "            # 将数据放到计算设备上，如 CPU 或 GPU\n",
    "            data = data.to(device)\n",
    "            # 前向传播，得到预测结果\n",
    "            output = model(data)\n",
    "            # 得到预测的类别，即概率最大的那一类\n",
    "            pred = output.argmax(dim=1, keepdim=True).item()\n",
    "            # 绘制图像和预测结果\n",
    "            plt.subplot(2, 5, i + 1)\n",
    "            plt.imshow(data.squeeze().cpu().numpy(), cmap='gray')\n",
    "            plt.title('Pred: {}'.format(pred))\n",
    "        plt.suptitle('Epoch: {}'.format(epoch))\n",
    "        plt.show()\n",
    "\n",
    "# 定义一个主函数\n",
    "def main():\n",
    "    # 定义计算设备，如 CPU 或 GPU\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    # 将模型放到计算设备上\n",
    "    model.to(device)\n",
    "    # 定义训练和测试的周期数\n",
    "    epochs = 50\n",
    "    # 循环进行训练和测试\n",
    "    for epoch in range(1, epochs + 1):\n",
    "        train(epoch)\n",
    "        test(epoch)\n",
    "    # 使用 matplotlib.pyplot.plot 绘制两个列表的折线图，并添加标题，标签和图例\n",
    "    plt.plot(train_acc, label='Train accuracy')\n",
    "    plt.plot(test_acc, label='Test accuracy')\n",
    "    plt.title('Accuracy of CNN on MNIST dataset')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy (%)')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# 检查当前模块是否为主模块\n",
    "if __name__ == '__main__':\n",
    "    # 调用主函数\n",
    "    main()   "
   ]
  }
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